Orthogonality, dependency, and music: An exploration of the relationships between music facets

Lee, Deborah; Robinson, Lyn; Bawden, David

doi:10.1002/asi.24426

Cited by 5 publications

(3 citation statements)

References 19 publications

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“…A detailed analysis of music facet orthogonality by Lee et al (2020) concluded that their three defining latent features of music are non-orthogonal. Hence, oblique rotation is appropriate to use.…”

Section: Task 1: Feature Space Classificationmentioning

confidence: 99%

Utilizing Machine Learning Algorithms Trained on AI-generated Synthetic Participant Recent Music-Listening Activity in Predicting Big Five Personality Traits

Solaiyappan,

Mohamdjawad,

et al. 2023

Preprint

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The recent rise of publicly available artificial intelligence (AI) tools such as ChatGPT hasraised a plethora of questions among users and skeptics alike. One major question asks, "Has AIgained the ability to indistinguishably mimic the psychology of its organic, human counterpart?".Since music has been known to be a positive predictor of personality traits due to the individuality ofpersonal preference, in this paper we use machine learning (ML) algorithms to analyze thepredictability of AI-generated or 'synthetic' participants' Big 5 personality traits (Openness,Conscientiousness, Extraversion, Agreeableness, and Neuroticism) using their recent music listeningactivity and motivations for listening to music. Recent music listening history for syntheticparticipants is generated using ChatGPT and the corresponding audio features for the songs arederived via the Spotify Application Programming Interface (Beats per minute, Danceability,Instrumentals, Happiness, etc). This study will also administer the Uses of Music Inventory toaccount for synthetic participants’ motivations for listening to music: emotional, cognitive, andbackground. The dataset will be trained and tested on scaler-model combinations to identify thepredictions with the least mean absolute error using ML models such as Random Forest, DecisionTree, K-Nearest Neighbors, Logistic Regression, and Support Vector Machine. Both regression(continuous numeric value) and classification (Likert scale option) prediction methods will be used.An Exploratory Factor Analysis (EFA) will be conducted on the audio features to find a latentrepresentation of the dataset that machine learning is also trained and tested on. A full literaturereview showed this is the first study to use both Spotify API data, rather than self-reported musicpreference, and machine learning, in addition to traditional statistical tests and regression models, topredict the personality of a synthetic college student demographic. The findings of this study showChatGPT struggles to mimic the diverse and complex nature of human personality psychology andmusic taste. This paper is a pilot study to a broader ongoing investigation where the findings ofsynthetic participants are compared to that of real college students using the same inventories forwhich data collection is ongoing.

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Section: Task 1: Feature Space Classificationmentioning

confidence: 99%

Utilizing Machine Learning Algorithms Trained on AI-generated Synthetic Participant Recent Music-Listening Activity in Predicting Big Five Personality Traits

Solaiyappan,

Mohamdjawad,

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…Furthermore, form is the primary facet in DDC art, whereas medium is the primary facet for music (according to both DDC (see above) and other classifications of music (Lee and Robinson 2018)), possibly symptomatic of a crossover. Even within music, medium and form/genre are blurred in their execution as facets, as they are shown to be dependent facets (Lee, Robinson, and Bawden 2021).…”

Section: Form and Form-like Ideasmentioning

confidence: 99%

Knowledge Organisation Across Aural and Visual Domains: Connecting the Facets of Music and Art

Lee¹,

Hider²

2022

Knowledge Organization Across Disciplines, Domains, Services and Technologies

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Music and art are complex areas of knowledge organisation, and this paper considers the connections between the classifications in both domains. It starts with an analysis of music and art facets, utilising the Dewey Decimal Classification and relevant KO literature. Four important connections emerge from the comparison: the interplay between medium and materials; the interweaving of forms, genres, corporeality, and medium; the idea of format and its links to physicality; the connections between iconography, music subjects and function. These findings as well as the resulting model of combined music-art facets, further our knowledge of music and art KO, as well as interdisciplinary classification.

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“…The computer simulation system is used to assist patients in singing songs, to achieve the purpose of respiratory training. From the above introduction, we need to take advantage of the artificial intelligence technology to innovate music therapy methods, enhance the accuracy of treatment schemes, and improve the development of the medical field ( Lee et al, 2021 ).…”

Section: Introductionmentioning

confidence: 99%

Long Short-Term Memory-Based Music Analysis System for Music Therapy

Zheng

et al. 2022

Front. Psychol.

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Music can express people’s thoughts and emotions. Music therapy is to stimulate and hypnotize the human brain by using various forms of music activities, such as listening, singing, playing and rhythm. With the empowerment of artificial intelligence, music therapy technology has made innovative development in the whole process of “diagnosis, treatment and evaluation.” It is necessary to make use of the advantages of artificial intelligence technology to innovate music therapy methods, ensure the accuracy of treatment schemes, and provide more paths for the development of the medical field. This paper proposes an long short-term memory (LSTM)-based generation and classification algorithm for multi-voice music data. A Multi-Voice Music Generation system called MVMG based on the algorithm is developed. MVMG contains two main steps. At first, the music data are modeled to the MDPI and text sequence data by using an autoencoder model, including music features extraction and music clip representation. And then an LSTM-based music generation and classification model is developed for generating and analyzing music in specific treatment scenario. MVMG is evaluated based on the datasets collected by us: the single-melody MIDI files and the Chinese classical music dataset. The experiment shows that the highest accuracy of the autoencoder-based feature extractor can achieve 95.3%. And the average F1-score of LSTM is 95.68%, which is much higher than the DNN-based classification model.

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Orthogonality, dependency, and music: An exploration of the relationships between music facets

Cited by 5 publications

References 19 publications

Utilizing Machine Learning Algorithms Trained on AI-generated Synthetic Participant Recent Music-Listening Activity in Predicting Big Five Personality Traits

Utilizing Machine Learning Algorithms Trained on AI-generated Synthetic Participant Recent Music-Listening Activity in Predicting Big Five Personality Traits

Knowledge Organisation Across Aural and Visual Domains: Connecting the Facets of Music and Art

Long Short-Term Memory-Based Music Analysis System for Music Therapy

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